四旋翼无人机安全轨迹跟踪控制

针对存在安全约束的四旋翼无人机,为了保证其能够快速稳定地跟踪给定轨迹,本文提出了一种基于双闭环思想及控制障碍函数求解二次规划问题的控制器设计框架.首先,考虑到无人机的模型不确定性及外界干扰问题,基于快速非奇异终端滑模面设计了双闭环标称控制器,能够实现有限时间快速收敛.进一步地,为了解决无人机遇到的状态、距离约束等安全控制问题,利用控制障碍函数,将带有约束的控制器设计问题转化成二次规划的求解问题.最后,对提出的控制策略进行了仿真,验证了控制器的快速性和鲁棒性,并实现了给定轨迹的安全跟踪.     

基于时变障碍李雅普诺夫函数的变体无人机有限时间控制

针对复杂扰动下可执行多种任务的复合式变体无人机, 提出了一种基于浸入与不变(Immersion and invariance, I&I)理论和隐含系统状态受限条件的复合时变障碍Lyapunov函数(Composite time-varying barrierLyapunov function, CTV-BLF)的控制方案. 设计了一种基于浸入与不变理论的扰动观测器, 构建了一种基于监督因子的有限时间动态尺度因子(Finite-time dynamic scaling factor, FT-DSF)调节器. 在此基础上, 设计了一种基于复合时变障碍Lyapunov函数和动态滑模面的控制器, 保证系统状态始终在约束条件之内. 通过衍生定理证明轨迹跟踪误差是有限时间稳定的. 最终仿真结果验证了所提方案的有效性.     

Adaptive finite‐time neural backstepping control for multi‐input and multi‐output state‐constrained nonlinear systems using tangent‐type nonlinear mapping

This article focuses on the problem of adaptive finite‐time neural backstepping control for multi‐input and multi‐output nonlinear systems with time‐varying full‐state constraints and uncertainties. A tan‐type nonlinear mapping function is first proposed to convert the strict‐feedback system into a new pure‐feedback one without constraints. Neural networks are utilized to cope with unknown functions. To improve learning performance, a composite adaptive law is designed using tracking error and approximate error. A finite‐time convergent differentiator is adopted to avoid the problem of “explosion of complexity.” By theoretical analysis, all the signals of system are proved to be bounded, the outputs can track the desired signals in a finite time, and full‐state constraints are not transgressed. Finally, comparative simulations are offered to confirm the validity of the proposed control scheme.     

随机激励下板球系统建模与有限时间全状态预设性能跟踪控制

研究板球系统受到随机激励时的数学建模与轨迹跟踪控制问题. 首次建立了板球系统的随机数学模型, 并 结合backstepping方法、有限时间预设性能函数、全状态约束及新的预设性能推导方法设计了具有未知输入饱和的 随机板球系统实际有限时间全状态预设性能跟踪控制器, 实现了随机激励下板球系统的有限时间预设性能轨迹跟 踪控制. 所设计的控制器保证了系统跟踪误差能够被预先给定的有限时间性能函数约束, 并且能在任意给定的停息 时间内收敛到预先给定的邻域内. 最后通过仿真实验验证了所设计控制器具有更好的控制效果.     

Observer-based sliding mode control for bilateral teleoperation with time-varying delays

In this paper, a tracking control scheme is investigated for a bilateral teleoperation system with time-varying delays and dynamic uncertainties. The tracking control scheme is based on an extended state observer (ESO), a time-delay part observer and a continuous terminal sliding mode control (CTSMC) strategy. The dynamic uncertainties are dealt with by the ESO for the bilateral teleoperation system. The time-varying delays with unmeasurable derivatives are estimated by the two time-delay part observers. The CTSMC strategy is used to ensure finite-time convergence for the bilateral teleoperation system without knowing the second derivatives of tracking errors. Finally, experiment results are shown for the bilateral teleoperation system to demonstrate effectiveness of the developed tracking control scheme.     

飞轮安装偏差的过驱动航天器有限时间姿态控制

针对反作用飞轮安装存在偏差的过驱动航天器姿态跟踪问题, 提出一种有限时间姿态补偿控制策略. 通过设计自适应滑模控制器保证实现对不确定性转动惯量与外部干扰的鲁棒控制, 同时实现对飞轮安装偏差的补偿控制, 并应用Lyapunov 稳定性理论证明了该控制器能够在有限时间内实现姿态跟踪控制. 最后, 将该控制器应用于某型航天器的姿态跟踪控制, 仿真结果验证了所提出方法的有效性.     

基于快速终端滑模面的两旋翼飞行器有限时间姿态控制

针对带有模型不确定和外部干扰的两旋翼飞行器,提出一种基于快速终端滑模面的有限时间自适应姿态控制方法,保证两旋翼飞行器对期望姿态角度的有限时间跟踪。构造快速终端滑模面,并设计分段连续函数避免滑模变量求导产生的奇异值问题。在此基础上,设计有限时间姿态控制器,并设计系统不确定上界的自适应更新律,抵消模型不确定性和外部干扰的影响。经李雅普诺夫方法证明滑模变量、姿态角误差、角速度误差等闭环信号最终一致有界,且有限时间收敛至平衡点邻域,收敛时间与系统状态变量初始值有关。最后,采用了矩形波和 曲线作为设定信号,设计相应的跟踪实验,并在两旋翼飞行器平台上验证所提控制方法的有效性,且分析双曲正切函数对系统控制输入影响,经实验测试其可减少系统颤振现象。     

带有输出约束的水面船舶实际有限时间控制

考虑带有输出约束的水面船舶系统,提出一种自适应神经网络航迹跟踪实际有限时间控制算法.基于反步法设计有限时间控制律,构造障碍李雅普诺夫函数处理输出约束问题,采用神经网络逼近船舶模型中的不确定信息.在控制算法递推过程中,通过设计一个关于跟踪误差的可微幂函数来避免控制器中的奇异问题.借助李雅普诺夫稳定性分析理论,证明了航迹跟踪误差在有限时间内收敛到有界的邻域内.最后,以一艘1:70的比例模型船作为仿真对象,来验证所提出的航迹跟踪实际有限时间控制算法的有效性.     

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.      

角速度约束下的刚体飞行器鲁棒有限时间姿态镇定

针对角速度状态受限条件下的刚体飞行器姿态镇定控制问题,提出一种基于扰动观测器的时变状态增益有限时间姿态控制方法.针对基于修正型罗德里格斯参数(MRPs)描述的刚体飞行器姿态控制模型,首先,利用齐次性理论并充分考虑到系统的模型结构特点,设计一种带有角速度约束项的有限时间姿态控制器,使得系统有限时间镇定;其次,在初始状态满足受限条件的情况下,角速度在任意时刻都可以被约束在期望的范围内;然后,针对存在外部干扰的姿态环动力学系统,提出一种带扰动估计补偿的复合有限时间姿态镇定控制器;最后,通过与其他两种控制方法的仿真比较,验证了所提出控制方案的有效性和优越性.     

Finite-time adaptive neural control for uncertain nonlinear time-delay systems with actuator delay and full-state constraints

This paper investigates finite-time adaptive neural tracking control for a class of nonlinear time-delay systems subject to the actuator delay and full-state constraints. The difficulty is to consider full-state time delays and full-state constraints in finite-time control design. First, finite-time control method is used to achieve fast transient performances, and new Lyapunov–Krasovskii functionals are appropriately constructed to compensate time delays, in which a predictor-like term is utilized to transform input delayed systems into delay-free systems. Second, neural networks are utilized to deal with the unknown functions, the Gaussian error function is used to express the continuously differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are employed to guarantee that full-state signals are restricted within certain fixed bounds. At last, based on finite-time stability theory and Lyapunov stability theory, the finite-time tracking control question involved in full-state constraints is solved, and the designed control scheme reduces learning parameters. It is shown that the presented neural controller ensures that all closed-loop signals are bounded and the tracking error converges to a small neighbourhood of the origin in a finite time. The simulation studies are provided to further illustrate the effectiveness of the proposed approach.     

Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints

This paper focuses on the adaptive finite-time neural network control problem for nonlinear stochastic systems with full state constraints. Adaptive controller and adaptive law are designed by backstepping design with log-type barrier Lyapunov function. Radial basis function neural networks are employed to approximate unknown system parameters. It is proved that the tracking error can achieve finite-time convergence to a small region of the origin in probability and the state constraints are confirmed in probability. Different from deterministic nonlinear systems, here the stochastic system is affected by two random terms including continuous Brownian motion and discontinuous Poisson jump process. Therefore, it will bring difficulties to the controller design and the estimations of unknown parameters. A simulation example is given to illustrate the effectiveness of the designed control method.     

Adaptive Fault‐Tolerant Attitude Tracking Control of Rigid Spacecraft on Lie Group with Fixed‐Time Convergence

This paper investigates the fixed‐time attitude tracking problem for rigid spacecraft in the presence of inertial uncertainties, external disturbances, actuator faults, and input saturation constraints. The logarithm map is first utilized to transform the tracking problem on SO(3) into the stabilization one on its associated Lie algebra ( ). A novel nonsingular fixed‐time‐based sliding mode is designed, which not only avoids the singularity but also guarantees that the convergence time of tracking errors along the sliding surface is independent of the state value. Then, an adaptive fault‐tolerant control law is constructed, in which an online adaptive law is incorporated to estimate the upper boundary of the lumped uncertainties. The combined control scheme enforces the system state to reach a neighborhood of the sliding surface in the sense of the fixed‐time concept. The key feature of the resulting control scheme is that it can accommodate actuator failures under limited control torque without the knowledge of fault information. Numerical simulations are finally performed to demonstrate the effectiveness of the proposed fixed‐time controllers.     

Observer‐based adaptive robust control of a class of nonlinear systems with dynamic uncertainties

In this paper, a discontinuous projection‐based adaptive robust control (ARC) scheme is constructed for a class of nonlinear systems in an extended semi‐strict feedback form by incorporating a nonlinear observer and a dynamic normalization signal. The form allows for parametric uncertainties, uncertain nonlinearities, and dynamic uncertainties. The unmeasured states associated with the dynamic uncertainties are assumed to enter the system equations in an affine fashion. A novel nonlinear observer is first constructed to estimate the unmeasured states for a less conservative design. Estimation errors of dynamic uncertainties, as well as other model uncertainties, are dealt with effectively via certain robust feedback control terms for a guaranteed robust performance. In contrast with existing conservative robust adaptive control schemes, the proposed ARC method makes full use of the available structural information on the unmeasured state dynamics and the prior knowledge on the bounds of parameter variations for high performance. The resulting ARC controller achieves a prescribed output tracking transient performance and final tracking accuracy in the sense that the upper bound on the absolute value of the output tracking error over entire time‐history is given and related to certain controller design parameters in a known form. Furthermore, in the absence of uncertain nonlinearities, asymptotic output tracking is also achieved. Copyright © 2001 John Wiley & Sons, Ltd.     

Adaptive fuzzy neural network finite-time command filtered control of n-link robotic systems with actuator saturation

The finite time tracking control of n-link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite-time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite-time command filtered control achieves finite-time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.     

Extended state observer-based attitude fault-tolerant control of rigid spacecraft

In this paper, a novel fault-tolerant attitude tracking control is proposed for a rigid spacecraft with uncertain inertia matrix, actuator faults, actuator misalignment and external disturbances. The uncertainty of the inertial matrix is caused by the rotation of solar panels, payload movement and fuel consumption, and actuator faults, which include partially failed and completely failed actuators. A novel extended state observer is proposed to estimate the total uncertainties and a fast nonsingular terminal sliding-mode control scheme is proposed to get a faster, higher control precision. Strict finite-time convergence and the concrete convergence time are given. Finally, all the states of the closed-loop system are guaranteed to converge to the corresponding region in a finite time by choosing appropriate parameters. Simulation and comparison results further show the effectiveness and advantages of this method.     

Robust adaptive control for a class of semi‐strict feedback systems with state and input constraints

This paper proposes a dynamic surface control (DSC)–based robust adaptive control scheme for a class of semi‐strict feedback systems with full‐state and input constraints. In the control scheme, a constraint transformation method is employed to prevent the transgression of the full‐state constraints. Specifically, the state constraints are firstly represented as the surface error constraints, then, an error transformation is introduced to convert the constrained surface errors into new equivalent variables without constraints. By ensuring the boundedness of the transformed variables, the violation of the state constraints can be prevented. Moreover, in order to obtain magnitude limited virtual control signal for the recursive design, the saturations are incorporated into the control law. The auxiliary design systems are constructed to analyze the effects of the introduced saturations and the input constraints. Rigorous theoretical analysis demonstrates that the proposed control law can guarantee all the closed‐loop signals are uniformly ultimately bounded, the tracking error converges to a small neighborhood of origin, and the full‐state constraints are not violated. Compared with the existing results, the key advantages of the proposed control scheme include: (i) the utilization of the constraint transformation can handle both time‐varying symmetric and asymmetric state constraints and static ones in a unified framework; (ii) the incorporation of the saturations permits the removal of a feasibility analysis step and avoids solving the constrained optimization problem; and (iii) the “explosion of complexity” in traditional backstepping design is avoided by using the DSC technique. Simulations are finally given to confirm the effectiveness of the proposed approach.     

Decentralized filtering adaptive constrained tracking control for interconnected nonlinear systems

This paper presents a novel decentralized filtering adaptive constrained tracking control framework for uncertain interconnected nonlinear systems. Each subsystem has its own decentralized controller based on the established decentralized state predictor. For each subsystem, a piecewise constant adaptive law will generate total uncertainty estimates by solving the error dynamics between the host system and decentralized state predictor with the neglection of unknowns, whereas a decentralized filtering control law is designed to compensate both local and mismatched uncertainties from other subsystems, as well as achieve the local objective tracking of the host system. The achievement of global objective depends on the achievement of local objective for each subsystem. In the control scheme, the nonlinear uncertainties are compensated for within the bandwidth of low‐pass filters, while the trade‐off between tracking and constraints violation avoidance is formulated as a numerical constrained optimization problem which is solved periodically. Priority is given to constraints violation avoidance at the cost of deteriorated tracking performance. The uniform performance bounds are derived for the system states and control inputs as compared to the corresponding signals of a bounded closed‐loop reference system, which assumes partial cancelation of uncertainties within the bandwidth of the control signal. Compared with model predictive control (MPC) and unconstrained controller, the proposed control architecture is capable of solving the tracking control problems for interconnected nonlinear systems subject to constraints and uncertainties.     

Nonsingular terminal sliding mode based finite-time control for spacecraft attitude tracking

This paper studies finite-time attitude tracking control problem of a rigid spacecraft system with external disturbances and inertia uncertainties. Firstly, a new finite-time attitude tracking control law is designed using nonsingular terminal sliding mode concepts. In the absence and presence of external disturbances and inertia uncertainties, this controller can drive the attitude and angular velocity tracking errors to reach zero in finite time. Secondly, a finite-time disturbance observer is introduced to estimate the disturbance, and a composite controller is developed which consists of a feedback control based on nonsingular terminal sliding mode method and compensation term based on finite-time disturbance observer. Finite-time convergence of attitude tracking errors and the stability of the closed-loop system is ensured by the Lyapunov approach. Numerical simulations on attitude control of spacecraft are also given to demonstrate the performance of the proposed controllers.     

具有全状态约束和未建模动态的严格反馈系统有限时间自适应动态面控制

针对一类具有全状态约束、未建模动态和动态扰动的严格反馈非线性系统,通过构造非线性滤波器,并利用Young’s不等式,提出一种新的有限时间自适应动态面控制方法.引入非线性映射处理全状态约束,将有约束系统变成无约束系统,利用径向基函数逼近未知光滑函数,利用辅助系统产生的动态信号处理未建模动态.对于变换后的系统,利用改进的动态面控制和有限时间方法设计的控制器结构简单,移去现有有限时间控制中出现的“奇异性”问题,可加快系统的收敛速度.理论分析表明,闭环系统中的所有信号在有限时间内有界,全状态不违背约束条件.数值算例的仿真结果表明,所提出的自适应动态面控制方案是有效的.